The use of advanced machine vision systems and their underlying software is increasingly employed in a variety of manufacturing and quality control processes. Machine vision enables quicker, more accurate and repeatable results to be obtained in the production of both mass-produced and custom products. Typical machine vision systems include one or more cameras (typically having solid-state charge couple device (CCD) or CMOS-based imaging elements) directed at an area of interest, a frame grabber/image processing elements that capture and transmit CCD images, a computer or onboard processing device, and a user interface for running the machine vision software application and manipulating the captured images, and appropriate illumination on the area of interest.
Many applications of machine vision involve the inspection of components and surfaces for defects that affect quality or determination of the relative position of a part in multiple degrees of freedom with respect to the field of view. Machine vision is also employed in varying degrees to assist in manipulating manufacturing engines in the performance of specific tasks. A particular task using machine vision is visual servoing of robots in which a robot end effector is guided to a target using a machine vision feedback. Other applications also employ machine vision to locate a stationary and/or moving pattern.
The advent of increasingly faster and higher-performance computers has enabled the development of machine vision tools that can perform complex calculations in analyzing the pose of a viewed part in multiple dimensions. Such tools enable a previously trained/stored image pattern to be acquired and registered/identified regardless of its viewed position. In particular, existing commercially available search tools can register such patterns transformed by at least three degrees of freedom, including at least three translational degrees of freedom (x and y-axis image plane and the z-axis) and two or more non-translational degree of freedom (rotation, for example) relative to a predetermined origin.
An object in 3D can be registered from a trained pattern using at least two discrete images of the object with dissimilar poses. There are a number of challenges to registering an object in three-dimensions from trained images using this approach. For example, when non-coplanar object features are imaged, different features of the acquired image undergo different transformations, and thus, a single affine transformation can no longer be relied upon to provide the registered pattern. In addition the object's highlights and shadows “move” as the part is rotated in 3D and the features undergo true perspective projection. Also, any self-occlusions in the acquired image will tend to appear as boundaries in the simultaneously acquired images. This fools the 2D vision system into assuming an acquired image has a different shape than the trained counterpart.
A powerful machine vision tool for registering objects in 2D is the well-known PatMax® system available from Cognex Corporation of Natick, Mass. This system allows the two-dimensional pose of a subject part to be registered and identified quite accurately from a trained pattern, regardless of rotation and scale. Advanced machine vision search tools such as PatMax® also have the ability to take advantage of the previous known position of a search subject or target. This narrows the search area to positions relatively near the last known location. Therefore, searching is relatively faster on the next cycle since a smaller area is searched. In addition, these search tools can tolerate partial occlusion of a pattern and changes in its illumination, adding further to their robustness with respect to less-advanced machine vision approaches.
PatMax® operates by first acquiring a coarse image of the object, and then refining this image using an iterative, reweighted least-squares approximation of the acquired image with respect the trained image that progressively reduces the relative error therebetween with each iteration. That is, the original coarse pose is initially compared to the trained data. The data that appears to match best is reweighted to give that data a higher value in the overall calculation. This serves to remove portions of the image that may represent occlusions, shadows or other inconsistencies, and focusing the analysis upon more-reliable portions of the feature. After reweighting, a least-squares statistical approximation is performed on this data to refine the approximation of location and position of the object in the field of view. After (for example) approximately four iterations, the final position and location is determined with high accuracy.
Two-dimensional (2D) machine vision tools, such as PatMax® are highly robust. It highly desirable to adapt the techniques employed by such tools to provide a machine vision system that could resolve a trained pattern in three dimensions (3D) characterized by as many as six degrees of freedom (x, y and z translational degrees and three corresponding rotational degrees), or more.